스킬 파일

Honeycomb Analysis

Name: Honeycomb Analysis
Author: incidentfox

Honeycomb observability analysis. Use when querying Honeycomb datasets, traces, or metrics. Provides scripts and query syntax reference for high-cardinality exploration.

incidentfox559 스타2026. 2. 19.

직업
카테고리: 데이터 분석

스킬 내용

Authentication

IMPORTANT: Credentials are injected automatically by a proxy layer. Do NOT check for HONEYCOMB_API_KEY in environment variables - it won't be visible to you. Just run the scripts directly; authentication is handled transparently.

Configuration environment variables you CAN check (non-secret):

HONEYCOMB_API_ENDPOINT - Honeycomb API endpoint (default: https://api.honeycomb.io)

MANDATORY: Statistics-First Investigation

NEVER dump raw events. Always follow this pattern:

STATISTICS → SAMPLE → PATTERNS → CORRELATE

Statistics First - Know volume, error rate, and top patterns before sampling
Strategic Sampling - Choose the right strategy based on statistics
Pattern Extraction - Cluster similar errors to find root causes

관련 스킬

Honeycomb Analysis | Skills Pool

python .claude/skills/observability-honeycomb/scripts/get_statistics.py DATASET [--time-range SECONDS] [--filter FILTER]

# Examples:
python .claude/skills/observability-honeycomb/scripts/get_statistics.py production --time-range 3600
python .claude/skills/observability-honeycomb/scripts/get_statistics.py api-requests --filter "http.status_code >= 500"

python .claude/skills/observability-honeycomb/scripts/run_query.py DATASET --calc CALCULATION [--breakdown FIELD] [--filter FILTER]

# Calculations: COUNT, SUM, AVG, MAX, MIN, P50, P75, P90, P95, P99, HEATMAP, COUNT_DISTINCT
# Examples:
python .claude/skills/observability-honeycomb/scripts/run_query.py production --calc COUNT
python .claude/skills/observability-honeycomb/scripts/run_query.py production --calc P99 --column duration_ms --breakdown service.name
python .claude/skills/observability-honeycomb/scripts/run_query.py production --calc COUNT --filter "http.status_code >= 500" --breakdown error.message

python .claude/skills/observability-honeycomb/scripts/list_datasets.py

# Output: List of datasets with names and last write times

Calculation	Description	Example
`COUNT`	Count events	Total requests
`SUM`	Sum a column	Total bytes transferred
`AVG`	Average value	Average duration
`MAX` / `MIN`	Extremes	Peak latency
`P50`, `P75`, `P90`, `P95`, `P99`	Percentiles	P99 latency
`HEATMAP`	Distribution	Latency heatmap
`COUNT_DISTINCT`	Unique values	Unique users
`RATE_AVG`, `RATE_SUM`, `RATE_MAX`	Rate per second	Requests/second

column = value          # Exact match
column != value         # Not equal
column > value          # Greater than
column >= value         # Greater or equal
column < value          # Less than
column <= value         # Less or equal
column exists           # Field exists
column does-not-exist   # Field missing
column contains "str"   # Contains substring
column starts-with "s"  # Starts with prefix
column in (a, b, c)     # In set

# Group by service
--breakdown service.name

# Multiple breakdowns
--breakdown service.name --breakdown http.method

# Trace fields
trace.trace_id
trace.span_id
trace.parent_id
duration_ms
name

# HTTP fields
http.method
http.url
http.status_code
http.host

# Service fields
service.name
service.version

# Error fields
error
error.message
exception.type
exception.message

┌─────────────────────────────────────────────────────────────┐
│ 1. STATISTICS FIRST (mandatory)                              │
│    python get_statistics.py <dataset>                        │
│    → Know volume, error rate, top patterns                   │
└─────────────────────────────────────────────────────────────┘
                             │
                             ▼
                     High Error Rate?
               ┌─────────────┴─────────────┐
               │                           │
       YES (>5%)                           NO
               │                           │
               ▼                           ▼
┌─────────────────────────────┐  ┌───────────────────────────────────────────┐
│ 2. FAST PATH                │  │ 2. TARGETED INVESTIGATION                 │
│    Query errors directly    │  │    Filter by specific criteria            │
│    python run_query.py      │  │    python run_query.py dataset            │
│    --filter "error=true"    │  │    --filter "duration_ms > 1000"          │
│    --breakdown error.message│  │    → Look for anomalies                   │
└─────────────────────────────┘  └───────────────────────────────────────────┘

Goal	Command
Start investigation	`get_statistics.py <dataset>`
Count errors	`run_query.py <dataset> --calc COUNT --filter "error=true"`
P99 latency by service	`run_query.py <dataset> --calc P99 --column duration_ms --breakdown service.name`
Error distribution	`run_query.py <dataset> --calc COUNT --filter "error=true" --breakdown error.message`
List datasets	`list_datasets.py`

python .claude/skills/observability-honeycomb/scripts/run_query.py <dataset> --list-slos

python .claude/skills/observability-honeycomb/scripts/run_query.py <dataset> --list-triggers

Honeycomb Analysis

Authentication

MANDATORY: Statistics-First Investigation

Honeycomb Analysis

Authentication

MANDATORY: Statistics-First Investigation

Available Scripts

PRIMARY INVESTIGATION SCRIPTS

get_statistics.py - ALWAYS START HERE

run_query.py - Custom Queries

list_datasets.py - Dataset Discovery

Honeycomb Query Concepts

Calculations (Aggregations)

Filters

Breakdowns (Group By)

Common Fields

Investigation Workflow

Standard Incident Investigation

Quick Commands Reference

SLOs and Triggers

Checking SLOs

Checking Triggers (Alerts)

Anti-Patterns to Avoid

Key Differences from Other Platforms

Grafana Dashboards

KPI Dashboard Design

Openclaw Secret Scanning Maintainer

Bluebubbles

Session Logs

Openclaw Qa Testing